On dynamic multi-objective optimization, classification and performance measures

Tantar, Emilia; Tantar, Alexandru-Adrian; Bouvry, Pascal

doi:10.1109/cec.2011.5949964

Cited by 22 publications

(28 citation statements)

References 11 publications

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“…Tantar et al [14] argued that Farina et al's classification, although of undisputed importance, does not capture or does not describe where the dynamic changes in DMOPs come from or the causes of the dynamic changes. Accordingly, they proposed the following intuitive classification for dynamic environments:…”

Section: B Cause-based Classificationmentioning

confidence: 97%

“…Unlike the case in static situations, the environmental parameter δ, e.g., constraints, may change in dynamic environments. An example in point is the dynamic MNK-landscapes illustrated in [14], where the environmental parameter (the number of interacting bits and their respective distribution on the string) changes over time. In this paper, we concentrate on the change of F and S, although we recognize that the change of x and δ may also occur in some special cases.…”

Section: Proposed Framework For Scalable Dmopsmentioning

confidence: 99%

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A framework of scalable dynamic test problems for dynamic multi-objective optimization

Jiang

Yang

2014

2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

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Dynamic multi-objective optimization has received increasing attention in recent years. One of striking issues in this field is the lack of standard test suites to determine whether an algorithm is capable of solving dynamic multi-objective optimization problems (DMOPs). So far, a large proportion of test functions commonly used in the literature have only two objectives. It is greatly needed to create scalable test problems for developing algorithms and comparing their performance for solving DMOPs. This paper presents a framework of constructing scalable dynamic test problems, where dynamism can be easily added and controlled, and the changing Pareto-optimal fronts are easy to understand and their landscapes are exactly known. Experiments are conducted to compare the performance of four state-of-the-art algorithms on several typical test functions derived from the proposed framework, which gives a better understanding of the strengths and weaknesses of these tested algorithms for scalable DMOPs.

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Section: B Cause-based Classificationmentioning

confidence: 97%

Section: Proposed Framework For Scalable Dmopsmentioning

confidence: 99%

A framework of scalable dynamic test problems for dynamic multi-objective optimization

Jiang

Yang

2014

2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

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“…In this case we are facing an online dynamic multi-objective formulation -a 4 th order formulation, following the classification proposed in [61] -that can be depicted as…”

Section: Multi-objective Dynamic Formulationmentioning

confidence: 99%

Cognition: A Tool for Reinforcing Security in Software Defined Networks

Tantar

Palattella

Avanesov

et al. 2014

Advances in Intelligent Systems and Computing

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Abstract. Security is one of the most important requirements for networks and serious concerns for network providers and users. Software Defined Networking offers to network managers new opportunities for deploying efficient security mechanisms. By means of applications and controller functionalities, SDN is able to provide a highly reactive network security monitoring, to perform comprehensive traffic analysis, and to enforce fine-grained dynamic access policies. In the present work, we show how such security mechanisms can be further enforced by applying cognitive functions at the SDN application plane. The proposed approach that finds its foundation into the control loops applied in Autonomic Managers Networks (AMNs), can efficiently enable secure and safe SDN scenarios.

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“…Recently Tantar et al [61] introduced performance measures that are based on performance measures used in quantifying the tracking quality in multi-object tracking problems. The performance measures are developed based on the optimal subpattern assignment (OSPA) measure that can be used to compare sets with different cardinality [55].…”

Section: Optimal Subpattern Assignment Measurementioning

confidence: 99%

Performance measures for dynamic multi-objective optimisation algorithms

Helbig

Engelbrecht

2013

Information Sciences

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When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), performance measures are required to quantify the performance of the algorithm and to compare one algorithm's performance against that of other algorithms. However, for dynamic multi-objective optimisation (DMOO) there are no standard performance measures. This article provides an overview of the performance measures that have been used so far. In addition, issues with performance measures that are currently being used in the DMOO literature are highlighted.

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On dynamic multi-objective optimization, classification and performance measures

Cited by 22 publications

References 11 publications

A framework of scalable dynamic test problems for dynamic multi-objective optimization

A framework of scalable dynamic test problems for dynamic multi-objective optimization

Cognition: A Tool for Reinforcing Security in Software Defined Networks

Performance measures for dynamic multi-objective optimisation algorithms

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